Sadra Naddaf-Sh
Papers
1
Total Citations
6
H-Index
1
About
Sadra Naddaf-Sh is a researcher at the intersection of manufacturing engineering and artificial intelligence, with a primary focus on advancing quality control in industrial processes through machine learning. His most notable contribution is the development of a machine learning-based framework for classifying defects in automotive arc stud welding (ASW), a critical automated process where weld failures can necessitate scrapping entire vehicle structures. In his 2023 paper on this topic, which has garnered 6 citations, Naddaf-Sh demonstrated how ML models can reliably detect low-quality welds in real time, offering a scalable solution to a longstanding manufacturing challenge. This work bridges the gap between traditional process monitoring and modern data-driven diagnostics, providing a pathway to reduce waste and improve structural integrity in automotive assembly. By applying classification algorithms to weld signature data, he has opened new avenues for non-destructive evaluation in high-stakes production environments. His research is particularly valuable for engineers seeking to integrate Industry 4.0 technologies into legacy manufacturing systems, and his findings continue to inform both academic studies and practical quality assurance protocols in the automotive sector.
Research Focus
Key Achievements
Top Papers
- 1